Hearing aid users are typically exposed to a variety of sound environments, such as speech, music, or noisy environment. Various techniques are known and used to classify a user's sound environment, e.g., the Baynesian classifier, the Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM). Based on the classified sound environment, the hearing assistance device can apply parameter settings appropriate for the sound environment to improve a user's listening experience.
Each of the known sound environment classification techniques, however, has less than 100% accuracy. As a result, the user's sound environment can be misclassified. This misclassification can result in parameter settings for the hearing assistance device that may not be optimal for the user's sound environment.
Accordingly, there is a need in the art for improved sound environment classification for hearing assistance devices.